- Huckins, Laura M
- Dobbyn, Amanda
- Ruderfer, Douglas M
- Hoffman, Gabriel
- Wang, Weiqing
- Pardiñas, Antonio F
- Rajagopal, Veera M
- Als, Thomas D
- T Nguyen, Hoang
- Girdhar, Kiran
- Boocock, James
- Roussos, Panos
- Fromer, Menachem
- Kramer, Robin
- Domenici, Enrico
- Gamazon, Eric R
- Purcell, Shaun
- CommonMind Consortium
- Schizophrenia Working Group of the Psychiatric Genomics Consortium
- iPSYCH-GEMS Schizophrenia Working Group
- Demontis, Ditte
- Børglum, Anders D
- Walters, James TR
- O'Donovan, Michael C
- Sullivan, Patrick
- Owen, Michael J
- Devlin, Bernie
- Sieberts, Solveig K
- Cox, Nancy J
- Im, Hae Kyung
- Sklar, Pamela
- Stahl, Eli A
- et al.
Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.